Hao Jinye, Zhang Hongming, Han Yamin, Wu Jie, Zhou Lixiang, Luo Zhibo, Du Yutong
College of Information Engineering, Northwest A&F University, Xianyang 712100, China.
Animals (Basel). 2023 Jul 29;13(15):2458. doi: 10.3390/ani13152458.
The accurate breeding of individual sheep has shown outstanding effectiveness in food quality tracing, prevention of fake insurance claims, etc., for which sheep identification is the key to guaranteeing its high performance. As a promising solution, sheep identification based on sheep face detection has shown potential effectiveness in recent studies. Unfortunately, the performance of sheep face detection has still been a challenge due to diverse background illumination, sheep face angles and scales, etc. In this paper, an effective and lightweight sheep face detection method based on an improved RetinaFace algorithm is proposed. In order to achieve an accurate and real-time detection of sheep faces on actual sheep farms, the original RetinaFace algorithm is improved in two main aspects. Firstly, to accelerate the speed of multi-scale sheep face feature extraction, an improved MobileNetV3-large with a switchable atrous convolution is optimally used as the backbone network of the proposed algorithm. Secondly, the channel and spatial attention modules are added into the original detector module to highlight important facial features of the sheep. This helps obtain more discriminative sheep face features to mitigate against the challenges of diverse face angles and scale in sheep. The experimental results on our collected real-world scenarios have shown that the proposed method outperforms others with an of 95.25%, an average precision of 96.00%, a model size of 13.20 M, an average processing time of 26.83 ms, and a parameter of 3.20 M.
对个体绵羊进行精准繁育在食品质量追溯、防范虚假保险索赔等方面已展现出卓越成效，而绵羊识别是确保其高性能的关键。作为一种有前景的解决方案，基于绵羊面部检测的绵羊识别在近期研究中已显示出潜在成效。遗憾的是，由于背景光照多样、绵羊面部角度和尺度各异等因素，绵羊面部检测的性能仍是一项挑战。本文提出了一种基于改进的RetinaFace算法的高效轻量级绵羊面部检测方法。为了在实际绵羊养殖场实现对绵羊面部的准确实时检测，对原始的RetinaFace算法在两个主要方面进行了改进。首先，为加快多尺度绵羊面部特征提取速度，最优地使用了带有可切换空洞卷积的改进型MobileNetV3-large作为所提算法的骨干网络。其次，在原始检测器模块中加入通道和空间注意力模块，以突出绵羊的重要面部特征。这有助于获得更具判别力的绵羊面部特征，从而应对绵羊面部角度和尺度多样的挑战。在我们收集的真实场景上的实验结果表明，所提方法在召回率为95.25%、平均精度为96.00%、模型大小为13.20M、平均处理时间为26.83ms以及参数为3.20M的情况下优于其他方法。
